Redundancy based feature selection for microarray data

By Lei Yu, Huan Liu. Knowledge Discovery and Data Mining, 2004

319 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/229aa312890126f1aec64fb6590b46c41cd2721d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Liu
2. Lei Yu
3. Zexuan Zhu
4. Manoranjan Dash
5. Yew-Soon Ong
6. Sonajharia Minz
7. Vipin Kumar
8. Jugal Kumar Kalita

In Molecular Biology

1. Yew-Soon Ong
2. Zexuan Zhu
3. Manoranjan Dash
4. Huan Liu
5. Lei Yu
6. Sonajharia Minz
7. Vasile Palade
8. Vipin Kumar

Citations in the corpus

• Markov blanket-embedded genetic algorithm for gene selection (2007, methodology): “In particular, we consider the FCBF [ 3 ,27], BIRS [39] and standard GA feature selection algorithms to compare the proposed MBEGA method.”
• MIFS-ND: A mutual information-based feature selection method (2014, background): “Many feature selection algorithms (Bhattacharyya & Kalita, 2013; Caruana & Freitag, 1994; Frohlich, Chapelle, & Scholkopf, 2003; Lin, Ying, Lee, & Lee, 2012; Nemati, Basiri, Ghasem-Aghaee, & Aghdam, 2009; Yu & Liu, 2004) have been proposed for classification.”
• A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data (2013, background): “[14] M. Dash, H. Liu, and H. Motoda, “Consistency Based Feature Selection,” Proc.”
• Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis (2011, background): “5 Decision Trees (DTs) [11,12], the linguistic rules generated by our fuzzy-based models are short and easy to be read.”
• A review of feature selection methods on synthetic data (2013, unknown): “This technique has found success in many different real-world applications like DNA microarray analysis [7], intrusion detection [8,9], text categorization [10,11] or information retrieval [12], including image retrieval [13] or music information retrieval [14].”
• Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, background): “…applications like text categorization (Yang and Pederson, 1997; Forman, 2003), image retrieval (Swets and Weng, 1995; Dy et al., 2003), genomic microarray analysis (Xing et al., 2001; Yu and Liu, 2004), customer relationship management (Ng and Liu, 2000), and intrusion detection (Lee et al., 2000).”
• Toward integrating feature selection algorithms for classification and clustering (2005, background): “Structural and functional data from analysis of the human genome has increased many folds in recent years, presenting enormous opportunities and challenges for data mining [91, 96].”
• Feature Selection: A literature Review (2014, background): “In the application perspective, many real-world applications like intrusion detection [100, 101], text categorization [95,102, 109, 110], DNA microarray analysis [103], music information retrieval [104], image retrieval [105], information retrieval [106], customer relationship management [107], Geno”